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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 22918

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Special Issue Editors

Dr. Sergiu Dan Stan

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Guest Editor

Associate Professor, Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: mechatronics; parallel robots; robot programming; design of mechatronic systems; CAD; CAM; mechanisms and dynamics of machines; modelling and simulation; MATLAB/Simulink; VR; optimization; genetic algorithms
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Prof. Dr. Milos Manic

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Guest Editor

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: applied research in computational intelligence algorithms, such as artificial neural networks, fuzzy logic systems, and unsupervised learning techniques in areas of energy, cyber security, human–machine interfacing, intelligent control systems, software-defined networks, robotics/mechatronics, visualizations, and others
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Dr. Milos Simonovic

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Guest Editor

Associate Professor, Department for Mechatronics and Control of MEF, Mechanical Engineering Faculty Niš, A. Medvedeva 14, 18000 Niš, Serbia
Interests: robotics/mechatronics; Industry 4.0; energy; rail track detection; machine vision; computer vision; vision-based obstacle detection; machine learning dataset generation for obstacle detection in railways
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the application of the computer vision (CV) in mechatronics technology. In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of computer vision in mechatronics technology. We encourage articles covering high-impact research results in the field of computer vision: image processing and analysis; machine learning techniques; sensors with applications in mechatronics/robotics systems, sports, additive manufacturing, CAD/CAM systems, UAVs, etc. Computer vision has a lot to offer to the world of sports and physical activities, providing new opportunities for applications to help training and competitors.

Both theoretical and experimental studies are welcome, as well as comprehensive reviews and survey articles.

Topics of interest for this Special Issue include but are not limited to:

Computer vision in mechatronics/robotics;
Obstacle detection;
Computer vision in sports;
Serial robots, parallel robots, vision technology, vision inspection, robot programming;
Action recognition in realistic sports
3D machine vision and additive manufacturing;
Performance indices of robots;
ROS and machine vision in mechatronics;
Computer vision and robotics for medical application;
Model-based control of mechatronic systems;
Control systems based on vision;
Mechatronics and machine vision in practice;
Computer vision in mechanical systems;
Computer vision in CAD/CAM engineering;
Computer vision for autonomous vehicles, unmanned air vehicles.

Dr. Sergiu Dan Stan
Prof. Dr. Miloš Manić
Prof. Dr. Miloš Simonović
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (11 papers)

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Research

19 pages, 6051 KiB

Open AccessArticle

A Vision-Based Micro-Manipulation System

by Oskars Vismanis, Janis Arents, Jurga Subačiūtė-Žemaitienė, Vytautas Bučinskas, Andrius Dzedzickis, Brijesh Patel, Wei-Cheng Tung, Po-Ting Lin and Modris Greitans

Appl. Sci. 2023, 13(24), 13248; https://0-doi-org.brum.beds.ac.uk/10.3390/app132413248 - 14 Dec 2023

Viewed by 655

Abstract

This research article outlines the design and methodology employed in the development of a vision-based micro-manipulation system, emphasizing its constituent components. While the system is initially tailored for applications involving living cells, its adaptability to other objects is highlighted. The integral components include an image enhancement module for data preparation, an object detector trained on the pre-processed data, and a precision micro-manipulator for actuating towards detected objects. Each component undergoes rigorous precision testing, revealing that the proposed image enhancement, when combined with the object detector, outperforms conventional methods. Additionally, the micro-manipulator shows excellent results for working with living cells the size of yeast. In the end, the components are also tested in a combined system as a proof-of-concept. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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13 pages, 3163 KiB

Open AccessArticle

A Hybrid Retina Net Classifier for Thermal Imaging

by Ventrapragada Teju, Kambhampati Venkata Sowmya, Srinivasa Rao Kandula, Anca Stan and Ovidiu Petru Stan

Appl. Sci. 2023, 13(14), 8525; https://0-doi-org.brum.beds.ac.uk/10.3390/app13148525 - 24 Jul 2023

Cited by 1 | Viewed by 942

Abstract

Thermal imaging is a cutting-edge technology which has the capability to detect objects in any environmental conditions, such as smoke, fog, smog, etc. This technology finds its importance mainly during nighttime since it does not require light to detect the objects. Applications of this technology span into various sectors, most importantly in border security to detect any incoming hazards. Object detection and classification are generally difficult with thermal imaging. In this paper, a one-stage deep convolution network-based object detection and classification called retina net is introduced. Existing surveys are based on object detection using infrared information obtained from the objects. This research is focused on detecting and identifying objects from thermal images and surveillance data. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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13 pages, 2387 KiB

Open AccessArticle

Object-Level Data Augmentation for Deep Learning-Based Obstacle Detection in Railways

by Marten Franke, Vaishnavi Gopinath, Danijela Ristić-Durrant and Kai Michels

Appl. Sci. 2022, 12(20), 10625; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010625 - 20 Oct 2022

Cited by 2 | Viewed by 1671

Abstract

This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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18 pages, 15384 KiB

Open AccessArticle

Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines

by Simone Comari and Marco Carricato

Appl. Sci. 2022, 12(15), 7835; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157835 - 04 Aug 2022

Cited by 2 | Viewed by 1767

Abstract

In this work, we present a vision system particularly suited to the automatic recognition of reels in the field of automatic packaging machines. The output of the vision system is used to guide the autonomous grasping of the reels by a robot for a subsequent manipulation task. The proposed solution is built around three different methods to solve the ellipse-detection problem in an image. Such methods leverage standard image processing and mathematical algorithms, which are tailored to the targeted application. An experimental campaign demonstrates the efficacy of the proposed approach, even in the presence of low computational power and limited hardware resources, as in the use-case at hand. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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19 pages, 21561 KiB

Open AccessArticle

Integration of Computer Vision and Convolutional Neural Networks in the System for Detection of Rail Track and Signals on the Railway

by Aleksandar Dragan Petrović, Milan Banić, Miloš Simonović, Dušan Stamenković, Aleksandar Miltenović, Gavrilo Adamović and Damjan Rangelov

Appl. Sci. 2022, 12(12), 6045; https://0-doi-org.brum.beds.ac.uk/10.3390/app12126045 - 14 Jun 2022

Cited by 4 | Viewed by 2234

Abstract

One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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20 pages, 3416 KiB

Open AccessArticle

An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks

by Miroslav Milovanović, Alexandru Oarcea, Saša Nikolić, Andjela Djordjević and Miodrag Spasić

Appl. Sci. 2022, 12(11), 5372; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115372 - 26 May 2022

Cited by 1 | Viewed by 1203

Abstract

Currently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating environmental stimuli and disturbances in operational environments of dynamic systems. The structure is based on the Orthogonal Endocrine Neural Network (OENN) and Artificial Orthogonal Glands (AOGs). The operational mechanism of each AOG acquires and processes environmental stimuli and generates artificial hormone concentration values at the gland output. These values are introduced to the appropriate OENN layer to provoke the network with collected environmental insights. To verify the applicability of the proposed structure on a complex dynamical nonlinear system, it was tested in a laboratory environment on the laboratory magnetic levitation system (MLS). The main experimental goal was to test the tracking performance of a levitation object when the new control logic was applied. The results were compared with two additional intelligent algorithms and a default linear quadratic (LQ) control logic. OENN + AOG structure showed improved tracking performances compared with traditional LQ control and better adaptability to environmental conditions compared with similar existing solutions. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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10 pages, 3487 KiB

Open AccessArticle

Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning

by Aleksandar Miltenović, Ivan Rakonjac, Alexandru Oarcea, Marko Perić and Damjan Rangelov

Appl. Sci. 2022, 12(11), 5327; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115327 - 25 May 2022

Cited by 5 | Viewed by 2906

Abstract

Gears are essential machine elements that are exposed to heavy loads. In some cases, gearboxes are critical elements since they serve as machine drivers that must operate almost every day for a more extended period, such as years or even tens of years. Any interruption due to gear failures can cause significant losses, and therefore it is necessary to have a monitoring system that will ensure proper operation. Tooth surface damage is a common occurrence in operating gears. One of the most common types of damage to teeth surfaces is pitting. It is necessary for normal gear operations to regularly determine the occurrence and span of a damaged tooth surface caused by pitting. In this paper, we propose a machine vision system as part of the inspection process for detecting pitting and monitoring its progression. The implemented inspection system uses a faster R-CNN network to identify and position pitting on a specific tooth, which enables monitoring. Prediction confidence values of pitting damage detection are between 99.5–99.9%, while prediction confidence values for teeth recognized as crucial for monitoring are between 97–99%. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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18 pages, 8306 KiB

Open AccessArticle

Object Detection Algorithm for Wheeled Mobile Robot Based on an Improved YOLOv4

by Yanxin Hu, Gang Liu, Zhiyu Chen and Jianwei Guo

Appl. Sci. 2022, 12(9), 4769; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094769 - 09 May 2022

Cited by 8 | Viewed by 2395

Abstract

In practical applications, the intelligence of wheeled mobile robots is the trend of future development. Object detection for wheeled mobile robots requires not only the recognition of complex surroundings, but also the deployment of algorithms on resource-limited devices. However, the current state of basic vision technology is insufficient to meet demand. Based on this practical problem, in order to balance detection accuracy and detection efficiency, we propose an object detection algorithm based on a combination of improved YOLOv4 and improved GhostNet in this paper. Firstly, the backbone feature extraction network of original YOLOv4 is replaced with the trimmed GhostNet network. Secondly, enhanced feature extraction network in the YOLOv4, ordinary convolution is supplanted with a combination of depth-separable and ordinary convolution. Finally, the hyperparameter optimization was carried out. The experimental results show that the improved YOLOv4 network proposed in this paper has better object detection performance. Specifically, the precision, recall, F1, mAP (0.5) values, and mAP (0.75) values are 88.89%, 87.12%, 88.00%, 86.84%, and 50.91%, respectively. Although the mAP (0.5) value is only 2.23% less than the original YOLOv4, it is higher than YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5 compared to 29.34%, 28.99%, 20.36%, and 18.64%, respectively. In addition, it outperformed YOLOv4 in terms of mAP (0.75) value and precision, and its model size is only 42.5 MB, a reduction of 82.58% when compared to YOLOv4’s model size. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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17 pages, 5399 KiB

Open AccessArticle

Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping

by Mohammad Mohammadzadeh Babr, Maryam Faghihabdolahi, Danijela Ristić-Durrant and Kai Michels

Appl. Sci. 2022, 12(7), 3655; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073655 - 05 Apr 2022

Cited by 1 | Viewed by 1890

Abstract

Instance segmentation of overlapping plants to detect their grasps for possible robotic grasping presents a challenging task due to the need to address the problem of occlusion. We addressed the problem of occlusion using a powerful convolutional neural network for segmenting objects with complex forms and occlusions. The network was trained with a novel dataset named the “occluded plants” dataset, containing real and synthetic images of plant cuttings on flat surfaces with differing degrees of occlusion. The synthetic images were created using the novel framework for synthesizing 2D images by using all plant cutting instances of available real images. In addition to the method for occlusion handling for overlapped plants, we present a novel method for determining the grasps of segmented plant cuttings that is based on conventional image processing. The result of the employed instance segmentation network on our plant dataset shows that it can accurately segment the overlapped plants, and it has a robust performance for different levels of occlusions. The presented plants’ grasp detection method achieved 94% on the rectangle metric which had an angular deviation of 30 degrees and an IoU of 0.50. The achieved results show the viability of our approach on plant species with an irregular shape and provide confidence that the presented method can provide a basis for various applications in the food and agricultural industries. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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20 pages, 4825 KiB

Open AccessArticle

Dealing with Low Quality Images in Railway Obstacle Detection System

by Staniša Perić, Marko Milojković, Sergiu-Dan Stan, Milan Banić and Dragan Antić

Appl. Sci. 2022, 12(6), 3041; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063041 - 16 Mar 2022

Cited by 3 | Viewed by 2307

Abstract

Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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15 pages, 5886 KiB

Open AccessArticle

Analyzing Benford’s Law’s Powerful Applications in Image Forensics

by Diana Crișan, Alexandru Irimia, Dan Gota, Liviu Miclea, Adela Puscasiu, Ovidiu Stan and Honoriu Valean

Appl. Sci. 2021, 11(23), 11482; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311482 - 03 Dec 2021

Cited by 3 | Viewed by 2404

Abstract

The Newcomb–Benford law states that in a set of natural numbers, the leading digit has a probability distribution that decays logarithmically. One of its major applications is the JPEG compression of images, a field of great interest for domains such as image forensics. In this article, we study JPEG compression from the point of view of Benford’s law. The article focuses on ways to detect fraudulent images and JPEG quality factors. Moreover, using the image’s luminance channel and JPEG coefficients, we describe a technique for determining the quality factor with which a JPEG image is compressed. The algorithm’s results are described in considerably more depth in the article’s final sections. Furthermore, the proposed idea is applicable to any procedure that involves the analysis of digital images and in which it is strongly suggested that the image authenticity be verified prior to beginning the analyzing process. Full article

(This article belongs to the Special Issue Computer Vision in Mechatronics Technology)

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